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Self-enforcing Strategic Demand Reduction.
Paul S. A. Reitsma,
Peter Stone, János A. Csirik, and
Michael L. Littman.
In Agent Mediated Electronic Commerce IV: Designing
Mechanisms and Systems, Lecture Notes in Artificial Intelligence, pp. 289–306, Springer Verlag, 2002.
[PDF]141.7kB [postscript]125.4kB
Auctions are an area of great academic and commercial interest, from tiny auctions for toys on eBay to multi-billion-dollar auctions held by governments for resources or contracts. Although there has been significant research on auction theory, especially from the perspective of auction mechanisms, studies of autonomous bidding agents and their interactions are relatively few and recent. This paper examines several autonomous agent bidding strategies in the context of FAucS, a faithful simulation of a complex FCC spectrum auction. We introduce punishing randomized strategic demand reduction (PRSDR), a novel bidding strategy by which bidders can partition available goods in a mutually beneficial way without explicit inter-agent communication. When all use PRSDR, bidders obtain significantly better results than when using a reasonable baseline approach. The strategy automatically detects and punishes non-cooperating bidders to achieve robustness in the face of agent defection, and performs well under alternative conditions. The PRSDR strategy is fully implemented and we present detailed empirical results.
@InCollection(AMEC02-rsdr,
author = "Paul S. A. Reitsma and Peter Stone and J\'{a}nos A. Csirik and Michael L. Littman",
title="Self-enforcing Strategic Demand Reduction",
booktitle="Agent Mediated Electronic Commerce {IV}: Designing Mechanisms and Systems",
series="Lecture Notes in Artificial Intelligence",
publisher="Springer Verlag",
volume=2531,
pages="289--306",
year="2002",
abstract={
Auctions are an area of great academic and
commercial interest, from tiny auctions for toys on
eBay to multi-billion-dollar auctions held by
governments for resources or contracts. Although
there has been significant research on auction
theory, especially from the perspective of auction
mechanisms, studies of autonomous bidding agents and
their interactions are relatively few and recent.
This paper examines several autonomous agent bidding
strategies in the context of FAucS, a faithful
simulation of a complex FCC spectrum auction. We
introduce punishing randomized strategic demand
reduction (PRSDR), a novel bidding strategy by
which bidders can partition available goods in a
mutually beneficial way without explicit inter-agent
communication. When all use PRSDR, bidders obtain
significantly better results than when using a
reasonable baseline approach. The strategy
automatically detects and punishes non-cooperating
bidders to achieve robustness in the face of agent
defection, and performs well under alternative
conditions. The PRSDR strategy is fully implemented
and we present detailed empirical results.
},
)
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